In view of the limitation of the difference method,the adjustment model of CPⅢprecise trigonometric leveling control network based on the parameter method was proposed in the present paper.The experiment results show...In view of the limitation of the difference method,the adjustment model of CPⅢprecise trigonometric leveling control network based on the parameter method was proposed in the present paper.The experiment results show that this model has a simple algorithm and high data utilization,avoids the negative influences caused by the correlation among the data acquired from the difference method and its accuracy is improved compared with the difference method.In addition,the strict weight of CPⅢprecise trigonometric leveling control network was also discussed in this paper.The results demonstrate that the ranging error of trigonometric leveling can be neglected when the vertical angle is less than 3 degrees.The accuracy of CPⅢprecise trigonometric leveling control network has not changed significantly before and after strict weight.展开更多
基金National Natural Science Foundation of China(No.41661091)。
文摘In view of the limitation of the difference method,the adjustment model of CPⅢprecise trigonometric leveling control network based on the parameter method was proposed in the present paper.The experiment results show that this model has a simple algorithm and high data utilization,avoids the negative influences caused by the correlation among the data acquired from the difference method and its accuracy is improved compared with the difference method.In addition,the strict weight of CPⅢprecise trigonometric leveling control network was also discussed in this paper.The results demonstrate that the ranging error of trigonometric leveling can be neglected when the vertical angle is less than 3 degrees.The accuracy of CPⅢprecise trigonometric leveling control network has not changed significantly before and after strict weight.
文摘为提高图像异型波形提取效率及准确性,提出基于卷积神经网络(Convolutional Neural Network,CNN)模型的图像快速定位处理方法,通过优化YOLOX模型结构对新建模型进行训练、测试及验证。结果表明:CP-YOLOX-48和CP-YOLOX-64两个模型验证集损失值较低且差异性较小,然而CP-YOLOX-64的浮点运算数比CP-YOLOX-48的运算值高了近1倍,故确定CP-YOLOX-48为最佳模型。与两种原始YOLOX模型相比,CP-YOLOX模型的图像处理效率、精准率(Accuracy)、召回率(Recall)及mAP(mean Average Precision)值均略高,且精准率大于90%,证实了CP-YOLOX-48模型具有较高的预测精度及提取效率。
